22 citations · 116 across the 14 of their papers we have counts for
5 papers · 1 filter
Comparing Human and Machine Bias in Face Recognition
Samuel Dooley, Ryan Downing, George Wei +10
Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceiv…
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova +3
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usuall…
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova +6
Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model. These attacks can be provably deflected using differentially private (DP…
Technical Challenges for Training Fair Neural Networks
Valeriia Cherepanova, Vedant Nanda, Micah Goldblum +2
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes setti…
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley +4
Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These…